A Random Forest Genomic Classifier for Tumor Agnostic Prediction of Response to Anti-PD1 Immunotherapy.

Tumor mutational burden (TMB), a surrogate for tumor neoepitope burden, is used as a pan-tumor biomarker to identify patients who may benefit from anti-program cell death 1 (PD1) immunotherapy, but it is an imperfect biomarker. Multiple additional genomic characteristics are associated with anti-PD1 responses, but the combined predictive value of these features and the added informativeness of each respective feature remains unknown. We evaluated whether machine learning (ML) approaches using proposed determinants of anti-PD1 response derived from whole exome sequencing (WES) could improve prediction of anti-PD1 responders over TMB alone. Random forest classifiers were trained on publicly available anti-PD1 data (n = 104), and subsequently tested on an independent anti-PD1 cohort (n = 69). Both the training and test datasets included a range of cancer types such as non-small cell lung cancer (NSCLC), head and neck squamous cell carcinoma (HNSCC), melanoma, and smaller numbers of patients from other tumor types. Features used include summaries such as TMB and number of frameshift mutations, as well as more gene-level features such as counts of mutations associated with immune checkpoint response and resistance. Both ML algorithms demonstrated area under the receiver-operator curves (AUC) that exceeded TMB alone (AUC 0.63 "human-guided," 0.64 "cluster," and 0.58 TMB alone). Mutations within oncogenes disproportionately modulate anti-PD1 responses relative to their overall contribution to tumor neoepitope burden. The use of a ML algorithm evaluating multiple proposed genomic determinants of anti-PD1 responses modestly improves performance over TMB alone, highlighting the need to integrate other biomarkers to further improve model performance.

Entinostat Decreases Immune Suppression to Promote Antitumor Responses in a HER2+ Breast Tumor Microenvironment.

Therapeutic combinations to alter immunosuppressive, solid tumor microenvironments (TME), such as in breast cancer, are essential to improve responses to immune checkpoint inhibitors (ICI). Entinostat, an oral histone deacetylase inhibitor, has been shown to improve responses to ICIs in various tumor models with immunosuppressive TMEs. The precise and comprehensive alterations to the TME induced by entinostat remain unknown. Here, we employed single-cell RNA sequencing on HER2-overexpressing breast tumors from mice treated with entinostat and ICIs to fully characterize changes across multiple cell types within the TME. This analysis demonstrates that treatment with entinostat induced a shift from a protumor to an antitumor TME signature, characterized predominantly by changes in myeloid cells. We confirmed myeloid-derived suppressor cells (MDSC) within entinostat-treated tumors associated with a less suppressive granulocytic (G)-MDSC phenotype and exhibited altered suppressive signaling that involved the NFκB and STAT3 pathways. In addition to MDSCs, tumor-associated macrophages were epigenetically reprogrammed from a protumor M2-like phenotype toward an antitumor M1-like phenotype, which may be contributing to a more sensitized TME. Overall, our in-depth analysis suggests that entinostat-induced changes on multiple myeloid cell types reduce immunosuppression and increase antitumor responses, which, in turn, improve sensitivity to ICIs. Sensitization of the TME by entinostat could ultimately broaden the population of patients with breast cancer who could benefit from ICIs.

Multi-omic profiling of lung and liver tumor microenvironments of metastatic pancreatic cancer reveals site-specific immune regulatory pathways.

BACKGROUND: The majority of pancreatic ductal adenocarcinomas (PDAC) are diagnosed at the metastatic stage, and standard therapies have limited activity with a dismal 5-year survival rate of only 8%. The liver and lung are the most common sites of PDAC metastasis, and each have been differentially associated with prognoses and responses to systemic therapies. A deeper understanding of the molecular and cellular landscape within the tumor microenvironment (TME) metastasis at these different sites is critical to informing future therapeutic strategies against metastatic PDAC. RESULTS: By leveraging combined mass cytometry, immunohistochemistry, and RNA sequencing, we identify key regulatory pathways that distinguish the liver and lung TMEs in a preclinical mouse model of metastatic PDAC. We demonstrate that the lung TME generally exhibits higher levels of immune infiltration, immune activation, and pro-immune signaling pathways, whereas multiple immune-suppressive pathways are emphasized in the liver TME. We then perform further validation of these preclinical findings in paired human lung and liver metastatic samples using immunohistochemistry from PDAC rapid autopsy specimens. Finally, in silico validation with transfer learning between our mouse model and TCGA datasets further demonstrates that many of the site-associated features are detectable even in the context of different primary tumors. CONCLUSIONS: Determining the distinctive immune-suppressive features in multiple liver and lung TME datasets provides further insight into the tissue specificity of molecular and cellular pathways, suggesting a potential mechanism underlying the discordant clinical responses that are often observed in metastatic diseases.

Aptamer-functionalized neural recording electrodes for the direct measurement of cocaine in vivo.

Cocaine is a highly addictive psychostimulant that acts through competitive inhibition of the dopamine transporter. In order to fully understand the region specific neuropathology of cocaine abuse and addiction, it is unequivocally necessary to develop cocaine sensing technology capable of directly measuring real-time cocaine transient events local to different brain regions throughout the pharmacokinetic time course of exposure. We have developed an electrochemical aptamer-based in vivo cocaine sensor on a silicon based neural recording probe platform capable of directly measuring cocaine from discrete brain locations using square wave voltammetry (SWV). The sensitivity of the sensor for cocaine follows a modified exponential Langmuir model relationship and complete aptamer-target binding occurs in < 2 sec and unbinding in < 4 sec. The resulting temporal resolution is a 75X increase from traditional microdialysis sampling methods. When implanted in the rat dorsal striatum, the cocaine sensor exhibits stable SWV signal drift (modeled using a logarithmic exponential equation) and is capable of measuring real-time in vivo response to repeated local cocaine infusion as well as systemic IV cocaine injection. The in vivo sensor is capable of obtaining reproducible measurements over a period approaching 3 hours, after which signal amplitude significantly decreases likely due to tissue encapsulation. Finally, aptamer functionalized neural recording probes successfully detect spontaneous and evoked neural activity in the brain. This dual functionality makes the cocaine sensor a powerful tool capable of monitoring both biochemical and electrophysiological signals with high spatial and temporal resolution.

Correction: Aptamer-functionalized neural recording electrodes for the direct measurement of cocaine in vivo.

Correction for 'Aptamer-functionalized neural recording electrodes for the direct measurement of cocaine in vivo' by I. Mitch Taylor et al., J. Mater. Chem. B, 2017, 5, 2445-2458.